The objective of this study was to describe an interviewer training and calibration method to evaluate oral health literacy using the Brazilian Rapid Estimate of Adult Literacy in Dentistry (BREALD-30) in epidemiological studies. An experienced researcher (gold standard) conducted all training sessions. The interviewer training and calibration sessions included three different phases: theoretical training, practical training, and calibration. In the calibration phase, six interviewers (dentists) independently assessed 15 videos of individuals who had different levels of oral health literacy. Accuracy and reproducibility were evaluated using the kappa coefficient and the intraclass correlation coefficient (ICC). The percentage of agreement for each word in the instrument was also calculated. After training, the kappa values were higher than 0.911 and 0.893 for intra- and inter-rater agreement, respectively. When the results were analyzed separately for the different levels of literacy, the lowest agreement rate was found when evaluating the videos of individuals with low literacy (K = 0.871), but still within the range considered to be near-perfect agreement. The ICC values were higher than 0.990 and 0.975 for intra- and inter-rater agreement, respectively. The lowest percentage of agreement was 86.6% for the word "hipoplasia" (hypoplasia). This interviewer training and calibration method proved to be feasible and effective. Therefore, it can be used as a methodological tool in studies assessing oral health literacy using the BREALD-30.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1590/1807-3107BOR-2016.vol30.0090 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Validity and Utility of a Risk Prediction Model for Wound Infection After Lower Third Molar Surgery.
Oral Dis
January 2025
Laboratory of Clinical Pharmaceutics & Therapeutics, Faculty of Pharmaceutical Sciences, Hokkaido University, Sapporo, Japan.
Objectives: To externally validate a clinical prediction model for surgical site infection (SSI) after lower third molar (L3M) surgery and evaluate its clinical usefulness.
Methods: We conducted a retrospective cohort study of patients who underwent L3M surgery at Hokkaido University Hospital. The study was designed to evaluate the historical and methodological transportability.
A novel nomogram based on the patient's clinical data and CT signs to predict poor outcomes in AIS patients.
PeerJ
January 2025
Department of Medical Imaging, Guangzhou Hospital of Integrated Traditional and Western Medicine, Guangzhou, China.
Background: The 2019 American Heart Association/American Stroke Association (AHA/ASA) guidelines strongly advise using non-contrast CT (NCCT) of the head as a mandatory test for all patients with suspected acute ischemic stroke (AIS) due to CT's advantages of affordability and speed of imaging. Therefore, our objective was to combine patient clinical data with head CT signs to create a nomogram to predict poor outcomes in AIS patients.
Methods: A retrospective analysis was conducted on 161 patients with acute ischemic stroke who underwent mechanical thrombectomy at the Guangzhou Hospital of Integrated Traditional and Western Medicine from January 2019 to June 2023.
Intraoperative Features Improve Model Risk Predictions After Coronary Artery Bypass Grafting.
Ann Thorac Surg Short Rep
September 2024
Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.
Background: Intraoperative physiologic parameters could offer predictive utility in evaluating risk of adverse postoperative events yet are not included in current standard risk models. This study examined whether the inclusion of continuous intraoperative data improved machine learning model predictions for multiple outcomes after coronary artery bypass grafting, including 30-day mortality, renal failure, reoperation, prolonged ventilation, and combined morbidity and mortality (MM).
Methods: The Society of Thoracic Surgeons (STS) database features and risk scores were combined with retrospectively gathered continuous intraoperative data from patients.
Machine Learning for Predicting Primary Graft Dysfunction After Lung Transplantation: An Interpretable Model Study.
Transplantation
January 2025
Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, China.
Background: Primary graft dysfunction (PGD) develops within 72 h after lung transplantation (Lung Tx) and greatly influences patients' prognosis. This study aimed to establish an accurate machine learning (ML) model for predicting grade 3 PGD (PGD3) after Lung Tx.
Methods: This retrospective study incorporated 802 patients receiving Lung Tx between July 2018 and October 2023 (640 in the derivation cohort and 162 in the external validation cohort), and 640 patients were randomly assigned to training and internal validation cohorts in a 7:3 ratio.
Predicting loss of independence among geriatric patients following gastrointestinal surgery.
Patient Saf Surg
January 2025
Department of Surgery, University of Virginia, Charlottesville, Virginia, USA.
Background: While existing risk calculators focus on mortality and complications, elderly patients are concerned with how operations will affect their quality of life, especially their independence. We sought to develop a novel clinically relevant and easy-to-use score to predict elderly patients' loss of independence after gastrointestinal surgery.
Methods: This retrospective cohort study included patients age ≥ 65 years enrolled in the American College of Surgeons National Surgical Quality Improvement Program database and Geriatric Pilot Project who underwent pancreatic, colorectal, or hepatic surgery (January 1, 2014- December 31, 2018).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!